coef.cv.stepSplitReg | R Documentation |
coef.cv.stepSplitReg
returns the coefficients for a cv.stepSplitReg object.
## S3 method for class 'cv.stepSplitReg' coef(object, group_index = NULL, ...)
object |
An object of class cv.stepSplitReg |
group_index |
Groups included in the ensemble. Default setting includes all the groups. |
... |
Additional arguments for compatibility. |
The coefficients for the cv.stepSplitReg object.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
cv.stepSplitReg
# Required Libraries library(mvnfast) # Setting the parameters p <- 100 n <- 30 n.test <- 500 sparsity <- 0.2 rho <- 0.5 SNR <- 3 # Generating the coefficient p.active <- floor(p*sparsity) a <- 4*log(n)/sqrt(n) neg.prob <- 0.2 nonzero.betas <- (-1)^(rbinom(p.active, 1, neg.prob))*(a + abs(rnorm(p.active))) # Correlation structure Sigma <- matrix(0, p, p) Sigma[1:p.active, 1:p.active] <- rho diag(Sigma) <- 1 true.beta <- c(nonzero.betas, rep(0 , p - p.active)) # Computing the noise parameter for target SNR sigma.epsilon <- as.numeric(sqrt((t(true.beta) %*% Sigma %*% true.beta)/SNR)) # Simulate some data set.seed(1) x.train <- mvnfast::rmvn(n, mu=rep(0,p), sigma=Sigma) y.train <- 1 + x.train %*% true.beta + rnorm(n=n, mean=0, sd=sigma.epsilon) x.test <- mvnfast::rmvn(n.test, mu=rep(0,p), sigma=Sigma) y.test <- 1 + x.test %*% true.beta + rnorm(n.test, sd=sigma.epsilon) # Stepwise Split Regularized Regression step.out <- cv.stepSplitReg(x.train, y.train, n_models = c(2, 3), max_variables = NULL, keep = 4/4, model_criterion = c("F-test", "RSS")[1], stop_criterion = c("F-test", "pR2", "aR2", "R2", "Fixed")[1], stop_parameter = 0.05, shrinkage = TRUE, alpha = 4/4, include_intercept = TRUE, n_lambda = 50, tolerance = 1e-2, max_iter = 1e5, n_folds = 5, model_weights = c("Equal", "Proportional", "Stacking")[1], n_threads = 1) step.coefficients <- coef(step.out, group_index = 1:step.out$n_models_optimal) step.predictions <- predict(step.out, x.test, group_index = 1:step.out$n_models_optimal) mspe.step <- mean((step.predictions-y.test)^2)/sigma.epsilon^2
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